A note about the complexity of minimizing Nesterov's smooth Chebyshev–Rosenbrock function

This short note considers and resolves the apparent contradiction between known worst-case complexity results for first- and second-order methods for solving unconstrained smooth nonconvex optimization problems and a recent note by Jarre [On Nesterov's smooth Chebyshev–Rosenbrock function, Optim. Methods Softw. (2011)] implying a very large lower bound on the number of iterations required to reach the solution's neighbourhood for a specific problem with variable dimension.     

基于模糊决策的风力发电产业评价研究

根据风力发电技术产业的特点,构建改进层次分析与模糊一致矩阵相结合的方案决策数学模型,该模型既可以减少确定判断矩阵的盲目性,又可以避开评价体系中各等级间的隶属度确定问题,使评价结果更加可靠,适合于信息尚不完整、受主观因素影响较强的评价。实例验证了这一模型的合理性、可行性和有效性,具有实用价值。     

锦屏二级水电站3#引水隧洞有盖重防渗固结灌浆试验

本文介绍了锦屏二级水电站3#引水隧洞有盖重防渗固结灌浆试验的施工方法和试验参数。通过防渗固结灌浆试验,总结出在灌浆施工中应及时采集的相关信息和采取的施工工艺,既保证了施工质量又保证了施工速度     

高职国际商务类专业毕业环节考核模式比较研究

随着越来越多的学者对高职院校毕业环节考核模式的合理性和可行性提出质疑,很多高职院校都纷纷开始进行一些新模式的探索和改革.对此就现状开展调查,并从结果入手,对目前高职院校国际商务专业群中所采用的多种考核模式进行分析和比较,探寻真正切合高职教育理念、符合国际商务专业群特点的理想考核模式,并为所有高职文科类专业提供借鉴和参考...     

数据库案例演示和实践相结合的教学法

数据库案例演示和实践相结合的教学法是对数据库这门实践性较强的学科教学方法的一种探讨,本文对如何运用这种教学方法进行了讲解,并介绍了详细的实施方案和影响方案实施的因素。     

高职计算机专业学生评价体系的探索

在德国职业教育的影响下,高职计算机专业教育开始推行任务驱动,工作过程导向的教育模式,但是其教育评价体系还没有与教学模式相统一,实现与对应就业岗位需求的完美结合。高职计算机专业教育评价体系包含了很多方面,本文主要从学生学习效果的评价体系进行探索。     

关联规则挖掘在教学评价中的应用研究

教学评价是院校最重要的工作之一,而科学的评教方法又显得尤为重要。本文介绍一种更适合于关系型数据库关联规则挖掘的、基于SQL的频繁项关联规则挖掘算法,并将其应用于教学评价。通过对大量的样本数据进行试验,得出了应用以往传统的教学评价方法所无法得到重要的教学参考信息。     

Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.     

模糊综合评判理论在研究性学习系统中的研究及应用

介绍了工程数学中的模糊综合评判理论以及相应树型向量推理算法,并将模糊综合推理机制运用到研究性学习系统中进行研究型应用。该系统中运用模糊综合评判对学习效果进行合理评判,得出综合成绩,以其作为改进研究性学习模式的参考评证,从而使研究性学习过程良性发展。     

遗传算法与文本特征选择方法相结合的研究

对文本特征选择的传统方法进行了深入分析并对各自的优缺点进行了详细的对比;其次,通过利用遗传算法自身的寻优特点,把它与传统特征选择方法相结合,并得出了算法。最后,在对各自算法进行了分析,表明了在性能上有了明显的提高。